Optimization of Intelligent Approach for Low-Cost INS/GPS Navigation System

Authors:
Kamal Saadeddin;Mamoun F. Abdel-Hafez;Mohammad A. Jaradat;Mohammad Amin Jarrah
Affiliations:
Department of Mechanical Engineering, American University of Sharjah, Sharjah, United Arab Emirates;Department of Mechanical Engineering, American University of Sharjah, Sharjah, United Arab Emirates;Department of Mechanical Engineering, American University of Sharjah, Sharjah, United Arab Emirates and Department of Mechanical Engineering, Jordan University of Science and Technology, Irbid, Jo ...;Department of Mechanical Engineering, American University of Sharjah, Sharjah, United Arab Emirates
Venue:
Journal of Intelligent and Robotic Systems
Year:
2014

Citing 5
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Abstract

Due to the inherent highly nonlinear vehicle state error dynamics obtained from low-cost inertial navigation system (INS) and Global Positioning System (GPS) along with the unknown statistical properties of these sensors, the optimality/accuracy of the classical Kalman filter for sensor fusion is not guaranteed. Therefore, in this paper, low-cost INS/GPS measurement integration is optimized based on different artificial intelligence (AI) techniques: Neural Networks (NN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) architectures. The proposed approaches are aimed at achieving high-accuracy vehicle state estimates. The architectures utilize overlapping windows for delayed input signals. Both the NN approaches and the ANFIS approaches are used once with overlapping position windows as the input and once with overlapping position and velocity windows as the input. Experimental tests are conducted to evaluate the performance of the proposed AI approaches. The achieved accuracy is presented and discussed. The study finds that using ANFIS, with both position and velocity as input, provides the best estimates of position and velocity in the navigation system. Therefore, the dynamic input delayed ANFIS approach is further analyzed at the end of the paper. The effect of the input window size on the accuracy of state estimation is also discussed.